



from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=13,   SCREAMING_SNAKE_CASE_=30,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=37,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=10,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=0.6,   SCREAMING_SNAKE_CASE_=None,   )       ->   str:
						UpperCAmelCase_:		Optional[Any]							      =     parent
						UpperCAmelCase_:		int							      =     batch_size
						UpperCAmelCase_:		Dict							      =     image_size
						UpperCAmelCase_:		List[Any]							      =     patch_size
						UpperCAmelCase_:		Tuple							      =     num_channels
						UpperCAmelCase_:		int							      =     is_training
						UpperCAmelCase_:		List[Any]							      =     use_labels
						UpperCAmelCase_:		Any							      =     hidden_size
						UpperCAmelCase_:		int							      =     num_hidden_layers
						UpperCAmelCase_:		int							      =     num_attention_heads
						UpperCAmelCase_:		Optional[int]							      =     intermediate_size
						UpperCAmelCase_:		Dict							      =     hidden_act
						UpperCAmelCase_:		str							      =     hidden_dropout_prob
						UpperCAmelCase_:		Optional[Any]							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		str							      =     type_sequence_label_size
						UpperCAmelCase_:		str							      =     initializer_range
						UpperCAmelCase_:		Optional[Any]							      =     mask_ratio
						UpperCAmelCase_:		int							      =     scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						UpperCAmelCase_:		Union[str, Any]							      =     (image_size // patch_size) ** 2
						UpperCAmelCase_:		Optional[Any]							      =     int(math.ceil((1 - mask_ratio) * (num_patches + 1)					)					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Dict							      =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

						UpperCAmelCase_:		Any							      =     None
						if self.use_labels:
									UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size],   self.type_sequence_label_size					)

						UpperCAmelCase_:		str							      =     self.get_config()

						return config, pixel_values, labels

			def      __snake_case       (self					)       ->   int:
						return ViTMAEConfig(
						    image_size=self.image_size,   patch_size=self.patch_size,   num_channels=self.num_channels,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   decoder_hidden_size=self.hidden_size,   decoder_num_hidden_layers=self.num_hidden_layers,   decoder_num_attention_heads=self.num_attention_heads,   decoder_intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   is_decoder=SCREAMING_SNAKE_CASE_,   initializer_range=self.initializer_range,   mask_ratio=self.mask_ratio,   )

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   int:
						UpperCAmelCase_:		List[str]							      =     TFViTMAEModel(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_,   training=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   int:
						UpperCAmelCase_:		List[str]							      =     TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     model(SCREAMING_SNAKE_CASE_,   training=SCREAMING_SNAKE_CASE_					)
						# expected sequence length = num_patches
						UpperCAmelCase_:		List[Any]							      =     (self.image_size // self.patch_size) ** 2
						UpperCAmelCase_:		Tuple							      =     self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, num_patches, expected_num_channels)					)

						# test greyscale images
						UpperCAmelCase_:		Union[str, Any]							      =     1
						UpperCAmelCase_:		Union[str, Any]							      =     TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
						UpperCAmelCase_:		int							      =     model(SCREAMING_SNAKE_CASE_,   training=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     self.patch_size**2
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, num_patches, expected_num_channels)					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		Optional[int]							      =     self.prepare_config_and_inputs()
						((UpperCAmelCase_)      ,							(UpperCAmelCase_)      ,							(UpperCAmelCase_)):		Tuple							      =     config_and_inputs
						UpperCAmelCase_:		List[str]							      =     {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_tf
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
			A      		=	{'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

			A      		=	False
			A      		=	False
			A      		=	False
			A      		=	False

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		List[Any]							      =     TFViTMAEModelTester(self					)
						UpperCAmelCase_:		str							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_,   has_text_modality=SCREAMING_SNAKE_CASE_,   hidden_size=37					)

			def      __snake_case       (self					)       ->   Any:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""					)
			def      __snake_case       (self					)       ->   str:
						pass

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Union[str, Any]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									self.assertIsInstance(model.get_input_embeddings(),   (tf.keras.layers.Layer)					)
									UpperCAmelCase_:		Any							      =     model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_,   tf.keras.layers.Layer					)					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Union[str, Any]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		List[str]							      =     inspect.signature(model.call					)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_:		Union[str, Any]							      =     [*signature.parameters.keys()]

									UpperCAmelCase_:		Union[str, Any]							      =     ["""pixel_values"""]
									self.assertListEqual(arg_names[:1],   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   str:
						# make the mask reproducible
						np.random.seed(2					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_:		Any							      =     int((config.image_size // config.patch_size) ** 2					)
						UpperCAmelCase_:		List[str]							      =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Tuple							      =     model_class(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		List[str]							      =     self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		str							      =     model(SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		List[Any]							      =     copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)					)
									UpperCAmelCase_:		List[Any]							      =     model(**SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Tuple							      =     outputs_dict[0].numpy()
									UpperCAmelCase_:		List[Any]							      =     outputs_keywords[0].numpy()

									self.assertLess(np.sum(np.abs(output_dict - output_keywords					)					),   1E-6					)

			def      __snake_case       (self					)       ->   Any:
						# make the mask reproducible
						np.random.seed(2					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_:		int							      =     int((config.image_size // config.patch_size) ** 2					)
						UpperCAmelCase_:		Optional[Any]							      =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)

						def prepare_numpy_arrays(SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Any							      =     {}
									for k, v in inputs_dict.items():
												if tf.is_tensor(SCREAMING_SNAKE_CASE_					):
															UpperCAmelCase_:		str							      =     v.numpy()
												else:
															UpperCAmelCase_:		str							      =     np.array(SCREAMING_SNAKE_CASE_					)

									return inputs_np_dict

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Any							      =     model_class(SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		str							      =     self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Optional[int]							      =     prepare_numpy_arrays(SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Optional[int]							      =     model(**SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)
									self.assert_outputs_same(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						# make masks reproducible
						np.random.seed(2					)

						UpperCAmelCase_:		int							      =     int((tf_model.config.image_size // tf_model.config.patch_size) ** 2					)
						UpperCAmelCase_:		Any							      =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)
						UpperCAmelCase_:		Any							      =     tf.constant(SCREAMING_SNAKE_CASE_					)

						# Add `noise` argument.
						# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
						UpperCAmelCase_:		Optional[int]							      =     tf_noise

						super().check_pt_tf_models(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   int:
						# make mask reproducible
						np.random.seed(2					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		Dict							      =     self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_:		Any							      =     {
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__					),)
						    for module_member_name in dir(SCREAMING_SNAKE_CASE_					)
						    if module_member_name.endswith("""MainLayer"""					)
						    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
						    and module_member_name[: -len("""MainLayer"""					)] == model_class.__name__[: -len("""Model"""					)]
						    for module_member in (getattr(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					),)
						    if isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(SCREAMING_SNAKE_CASE_,   """_keras_serializable""",   SCREAMING_SNAKE_CASE_					)
						}

						UpperCAmelCase_:		Optional[int]							      =     int((config.image_size // config.patch_size) ** 2					)
						UpperCAmelCase_:		Union[str, Any]							      =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)
						UpperCAmelCase_:		Dict							      =     tf.convert_to_tensor(SCREAMING_SNAKE_CASE_					)
						inputs_dict.update({"""noise""": noise}					)

						for main_layer_class in tf_main_layer_classes:
									UpperCAmelCase_:		List[Any]							      =     main_layer_class(SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		Optional[Any]							      =     {
									    name: tf.keras.Input(tensor.shape[1:],   dtype=tensor.dtype					) for name, tensor in inputs_dict.items()
									}

									UpperCAmelCase_:		Any							      =     tf.keras.Model(SCREAMING_SNAKE_CASE_,   outputs=main_layer(SCREAMING_SNAKE_CASE_					)					)
									UpperCAmelCase_:		Dict							      =     model(SCREAMING_SNAKE_CASE_					)

									with tempfile.TemporaryDirectory() as tmpdirname:
												UpperCAmelCase_:		str							      =     os.path.join(SCREAMING_SNAKE_CASE_,   """keras_model.h5"""					)
												model.save(SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[Any]							      =     tf.keras.models.load_model(
												    SCREAMING_SNAKE_CASE_,   custom_objects={main_layer_class.__name__: main_layer_class}					)
												assert isinstance(SCREAMING_SNAKE_CASE_,   tf.keras.Model					)
												UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_					)
												self.assert_outputs_same(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			@slow
			def      __snake_case       (self					)       ->   List[str]:
						# make mask reproducible
						np.random.seed(2					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_:		Tuple							      =     int((config.image_size // config.patch_size) ** 2					)
						UpperCAmelCase_:		Optional[Any]							      =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Tuple							      =     model_class(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Union[str, Any]							      =     self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Dict							      =     model(SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)

									if model_class.__name__ == "TFViTMAEModel":
												UpperCAmelCase_:		int							      =     outputs.last_hidden_state.numpy()
												UpperCAmelCase_:		List[Any]							      =     0
									else:
												UpperCAmelCase_:		int							      =     outputs.logits.numpy()
												UpperCAmelCase_:		List[str]							      =     0

									with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(SCREAMING_SNAKE_CASE_,   saved_model=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		str							      =     model_class.from_pretrained(SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Any							      =     model(SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)

												if model_class.__name__ == "TFViTMAEModel":
															UpperCAmelCase_:		List[Any]							      =     after_outputs["""last_hidden_state"""].numpy()
															UpperCAmelCase_:		List[Any]							      =     0
												else:
															UpperCAmelCase_:		str							      =     after_outputs["""logits"""].numpy()
															UpperCAmelCase_:		Dict							      =     0

												UpperCAmelCase_:		List[Any]							      =     np.amax(np.abs(out_a - out_a					)					)
												self.assertLessEqual(SCREAMING_SNAKE_CASE_,   1E-5					)

			def      __snake_case       (self					)       ->   str:
						# make mask reproducible
						np.random.seed(2					)

						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_:		Dict							      =     int((config.image_size // config.patch_size) ** 2					)
						UpperCAmelCase_:		Any							      =     np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		List[str]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Dict							      =     self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Dict							      =     model.get_config()
									# make sure that returned config is jsonifiable, which is required by keras
									json.dumps(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		int							      =     model_class.from_config(model.get_config()					)
									# make sure it also accepts a normal config
									UpperCAmelCase_:		Optional[int]							      =     model_class.from_config(model.config					)
									UpperCAmelCase_:		Optional[Any]							      =     new_model(SCREAMING_SNAKE_CASE_					)  # Build model
									new_model.set_weights(model.get_weights()					)
									UpperCAmelCase_:		str							      =     new_model(SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)

									self.assert_outputs_same(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""					)
			def      __snake_case       (self					)       ->   int:
						pass

			@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""					)
			def      __snake_case       (self					)       ->   Dict:
						pass

			@slow
			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Any							      =     TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""					)
						self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
			return image







@require_tf
@require_vision
class 				_a				(   unittest.TestCase					):

			@cached_property
			def      __snake_case       (self					)       ->   Optional[int]:
						return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""					) if is_vision_available() else None

			@slow
			def      __snake_case       (self					)       ->   Tuple:
						# make random mask reproducible across the PT and TF model
						np.random.seed(2					)

						UpperCAmelCase_:		Dict							      =     TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""					)

						UpperCAmelCase_:		Any							      =     self.default_image_processor
						UpperCAmelCase_:		Optional[Any]							      =     prepare_img()
						UpperCAmelCase_:		Optional[Any]							      =     image_processor(images=SCREAMING_SNAKE_CASE_,   return_tensors="""tf"""					)

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						UpperCAmelCase_:		Any							      =     ViTMAEConfig()
						UpperCAmelCase_:		Optional[int]							      =     int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2					)
						UpperCAmelCase_:		str							      =     np.random.uniform(size=(1, num_patches)					)

						# forward pass
						UpperCAmelCase_:		str							      =     model(**SCREAMING_SNAKE_CASE_,   noise=SCREAMING_SNAKE_CASE_					)

						# verify the logits
						UpperCAmelCase_:		Dict							      =     tf.convert_to_tensor([1, 196, 768]					)
						self.assertEqual(outputs.logits.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[Any]							      =     tf.convert_to_tensor(
						    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]					)

						tf.debugging.assert_near(outputs.logits[0, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)











import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






